Time series data refers to a group of data points that are recorded at successive points in time, typically at uniform intervals, which, when combined, can help to show trends and patterns over time.
Temperature readings taken daily over decades and daily stock market closing prices are two examples of time series data. We could use this data to understand the changing climate over time or make informed investment decisions, respectively.
Reasons we might use this data can include:
- Analysis of trends only identifiable across wide time windows (Zhang, 2019)
- Forecasting of future values based on historical observations
- Detecting anomalies (Blazquez-Garcia et al., 2021)
- Understanding cyclical patterns (Aghabozorgi, Shirkhorshidi & Wah, 2015)
- Improving decision-making (Cox & Brodhead, 2021)
Time Series Data Examples
1. Stock Prices Trends
Stock prices represent the value of a company’s shares over time. They are recorded at regular intervals, typically daily, reflecting the market’s valuation of a company on a particular day. Analyzing these time series data can help investors make decisions about buying or selling stocks based on historical performance and predicted trends (Liu & Long, 2020).
2. Historical Weather Data
Meteorological stations record various weather parameters, such as temperature, humidity, and rainfall, at regular intervals throughout the day. This continuous stream of data helps meteorologists track and forecast weather patterns, allowing us to understand climatic changes and prepare for extreme weather events (Dimri, Ahmad, & Sharif, 2020).
3. Ongoing Electricity Consumption
Utility companies monitor and record the amount of electricity consumed by households and businesses over time, usually on an hourly basis. By analyzing this time series data, they can forecast demand, detect anomalies, and manage power distribution more efficiently to avoid outages and ensure a stable supply.
4. Weekly Website Traffic
Websites log the number of visitors and page views they receive over specific intervals, such as hourly or daily. This data helps webmasters and marketers understand user behavior, peak usage times, and the effectiveness of promotional campaigns, allowing for better decision-making and optimization strategies.
5. Annual Retail Sales
Retailers record sales transactions over time, capturing data points like the number of items sold, total revenue, and customer demographics. Analyzing these time series data provides insights into purchasing trends, seasonal variations, and the impact of promotions or events on sales.
6. Hospital Admissions Over Time
Hospitals track the number of patients admitted and discharged over time, often categorized by ailment or department. By reviewing this time series data, healthcare administrators can manage staffing levels, ensure adequate resources, and predict potential surges in patient inflow.
7. Air Quality Index (AQI) Over Time
Environmental monitoring stations measure the concentration of various pollutants in the air at regular intervals. This data helps authorities and the public understand the quality of the air they breathe, allowing for timely interventions, health advisories, and policy changes to combat pollution.
8. Daily Currency Exchange Rates
Central banks and financial institutions record the value of one currency relative to another on a daily basis. Analyzing these time series data helps traders, businesses, and travelers understand market dynamics, forecast economic shifts, and make informed financial decisions.
9. Daily Commute Times
Transportation departments might collect data on how long it takes for individuals to travel between two points during different times of the day. By examining this time series data, city planners can understand traffic patterns, identify congestion points, and design infrastructure improvements.
10. Seasonal Agricultural Crop Yields
Farmers and agricultural organizations track the amount of a particular crop produced per unit of land over each growing season. This data, when analyzed over time, provides insights into the effects of weather patterns, soil health, and farming practices on crop production.
11. Social Media Engagement Over Time
Social media platforms record metrics such as likes, shares, and comments on posts over time. By studying this time series data, content creators and businesses can gauge audience engagement, track the virality of content, and adapt their social media strategies for better reach and impact.
12. Seasonal Hospitality Bookings
Hotels and resorts track room bookings and occupancy rates over specific periods, such as daily or weekly. By analyzing this time series data, management can identify peak tourist seasons, optimize pricing strategies, and forecast future occupancy to improve operational efficiency.
13. Streaming Service Playcounts
Platforms like Spotify or Netflix log the number of times a song, movie, or show is played over time. This data helps content providers and artists understand trends in user preferences, the success of new releases, and tailor future content recommendations.
14. Athletic Performance Metrics over Time
Athletes and teams might record statistics like running times, goals scored, or other performance indicators across matches or training sessions. By examining this time series data, coaches can track progress, plan training regimens, and develop strategies for upcoming competitions.
15. Industrial Production Output
Factories and manufacturing units monitor the quantity of products they produce over regular intervals, such as daily or monthly. Analyzing this time series data helps business leaders identify production bottlenecks, optimize supply chain management, and anticipate market demand.
Benefits of Time Series Data
Time series data offers a wealth of insights by providing a sequential record of observations taken at regular intervals over time.
One of its primary benefits is the ability to identify trends and patterns (Liu et al., 2021; Zhang, 2019). For instance, businesses can discern seasonal variations in sales or user engagement, enabling them to make informed decisions about inventory management, marketing campaigns, or staffing needs. Recognizing patterns is paramount, not just for understanding past behaviors but also for predicting future ones.
Another significant advantage of time series data is its capacity for anomaly detection (Blazquez-Garcia et al., 2021). By consistently monitoring and analyzing these data streams, unexpected deviations from established patterns can be quickly identified. For instance, utility companies can pinpoint power consumption anomalies that suggest power theft or equipment malfunction. Similarly, healthcare professionals might notice unusual patterns in patient admissions, prompting investigations into potential disease outbreaks. These timely detections allow for rapid responses, minimizing potential damages or accelerating interventions.
Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-series clustering–a decade review. Information systems, 53, 16-38. (Source)
Blazquez-Garcia, A., Conde, A., Mori, U., & Lozano, J. A. (2021). A review on outlier/anomaly detection in time series data. ACM Computing Surveys (CSUR), 54(3), 1-33. (Source)
Cox, D. J., & Brodhead, M. T. (2021). A proof of concept analysis of decision-making with time-series data. The Psychological Record, 71(3), 349-366. (Source)
Dimri, T., Ahmad, S., & Sharif, M. (2020). Time series analysis of climate variables using seasonal ARIMA approach. Journal of Earth System Science, 129, 1-16. (Source)
Liu, H., & Long, Z. (2020). An improved deep learning model for predicting stock market price time series. Digital Signal Processing, 102, 102741. (Source)
Liu, Z., Zhu, Z., Gao, J., & Xu, C. (2021). Forecast methods for time series data: a survey. Ieee Access, 9, 91896-91912. (Source)
Zhang, X. (2019). Behavioral Responses to Surprise in Multi-level, Team-centric Organizations: The Case of Post-disaster Debris Removal Operations. Rensselaer Polytechnic Institute.
Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]